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The feedforward (FFW) layers in standard transformer architectures incur a linear increase in computational costs and activation memory as the hidden layer width grows. Sparse mixture-of-experts (MoE)...

by Yadollah Ranjbar-Slamloo, Tsukasa Kamigaki Goal-directed behavior requires sensory information to be transformed into motor commands, yet the precise temporal structure of communication between sensory and motor cortices during this process remains poorly understood. By simultaneously recording spiking activity from primary somatosensory cortex (S1) and secondary motor cortex (M2) in mice performing a tactile delayed go/no-go task, we identified a brief, stimulus-triggered communication window that predicted behavioral success. Time-shifted canonical correlation analysis revealed a sharp, feedforward surge in S1 → M2 coupling peaking at a short latency, as expected for a potentially direct transmission pathway. This transient interaction conveyed stimulus-selective information such that the S1 → M2 communication subspace aligned with stimulus-coding axes in both regions. In contrast, M2 → S1 feedback interactions were weaker and delayed. Brief optogenetic suppression of S1 reduced both M2 stimulus selectivity and behavioral discriminability, consistent with a functional role of the transient S1 → M2 coupling. These findings demonstrate that sensorimotor transformation is supported not by sustained linear coupling along the stimulus- or choice- axis, but by a short-lived and highly selective feedforward communication channel that rapidly transmits reliable sensory representations into motor-planning circuits.

Context profoundly shapes neural responses and behavior. During context-dependent sensory processing, recurrent connections shape the integration of feedforward sensory input and feed-back input from ...

by Yadollah Ranjbar-Slamloo, Tsukasa Kamigaki Goal-directed behavior requires sensory information to be transformed into motor commands, yet the precise temporal structure of communication between sensory and motor cortices during this process remains poorly understood. By simultaneously recording spiking activity from primary somatosensory cortex (S1) and secondary motor cortex (M2) in mice performing a tactile delayed go/no-go task, we identified a brief, stimulus-triggered communication window that predicted behavioral success. Time-shifted canonical correlation analysis revealed a sharp, feedforward surge in S1 → M2 coupling peaking at a short latency, as expected for a potentially direct transmission pathway. This transient interaction conveyed stimulus-selective information such that the S1 → M2 communication subspace aligned with stimulus-coding axes in both regions. In contrast, M2 → S1 feedback interactions were weaker and delayed. Brief optogenetic suppression of S1 reduced both M2 stimulus selectivity and behavioral discriminability, consistent with a functional role of the transient S1 → M2 coupling. These findings demonstrate that sensorimotor transformation is supported not by sustained linear coupling along the stimulus- or choice- axis, but by a short-lived and highly selective feedforward communication channel that rapidly transmits reliable sensory representations into motor-planning circuits.

Feedforward neural networks with sigmoid activations are of fundamental importance in neural computation. However, our understanding of the circuit complexity of such networks for implementing Boolean functions remains limited. The analog output of the sigmoid, along with its transcendental nature, leads to unique mathematical challenges for proving lower bounds. Introducing new techniques, we establish new upper and lower bounds on the complexity of sigmoidal networks computing Boolean functions: \begin{itemize} \item We prove nearly tight bounds for the wire complexity of computing PARITY with such networks of constant depth and polynomial-bounded weights. \item We extend the size lower bound of Kane and Williams [STOC'16] against depth-$3$ threshold circuits, to depth-3 sigmoidal networks with polynomial weights, proving a $\Omega(n^{1.5}/ (\log n)^{O(1)})$ size lower bound for computing an explicit function in P. \item We prove that every symmetric function on $n$ bits can be computed by a depth-$3$ sigmoidal network with $O(n)$ wires and polynomial weights. This stands in stark contrast with threshold circuits of $d=O(1)$ depth, where $n^{1+\alpha_d}$-wire lower bounds are known for $\alpha_d > 0$. \end{itemize} We also answer a few fundamental problems related to sigmoidal networks, including proving new simulations and separations between threshold circuits and sigmoidal networks. We establish these results using a variety of techniques, including random restrictions for sigmoid gates, analyzing the average sensitivity of networks with sigmoids, simulation arguments, and using tools from approximation theory, complex analysis, and algebraic geometry.

by Yadollah Ranjbar-Slamloo, Tsukasa Kamigaki Goal-directed behavior requires sensory information to be transformed into motor commands, yet the precise temporal structure of communication between sensory and motor cortices during this process remains poorly understood. By simultaneously recording spiking activity from primary somatosensory cortex (S1) and secondary motor cortex (M2) in mice performing a tactile delayed go/no-go task, we identified a brief, stimulus-triggered communication window that predicted behavioral success. Time-shifted canonical correlation analysis revealed a sharp, feedforward surge in S1 → M2 coupling peaking at a short latency, as expected for a potentially direct transmission pathway. This transient interaction conveyed stimulus-selective information such that the S1 → M2 communication subspace aligned with stimulus-coding axes in both regions. In contrast, M2 → S1 feedback interactions were weaker and delayed. Brief optogenetic suppression of S1 reduced both M2 stimulus selectivity and behavioral discriminability, consistent with a functional role of the transient S1 → M2 coupling. These findings demonstrate that sensorimotor transformation is supported not by sustained linear coupling along the stimulus- or choice- axis, but by a short-lived and highly selective feedforward communication channel that rapidly transmits reliable sensory representations into motor-planning circuits.

Cardiovascular regulation during exercise has traditionally been explained by the interaction between central command, a feedforward neural mechanism originating in higher brain centers, and peripheral cardiovascular reflexes, particularly the exercise pressor reflex and arterial baroreflex. Although central command is widely recognized as initiating autonomic adjustments in proportion to perceived effort, accumulating evidence indicates that its role extends well beyond the generation of parallel autonomic drive. In this review, we synthesize experimental findings spanning spinal and brainstem neurophysiology to integrative studies in intact animals and humans, showing that descending signals associated with central command may access the neural circuitry involved in processing peripheral cardiovascular information and regulating autonomic output. Neurophysiological studies identify multiple sites of interaction, including inhibitory gating of group III/IV muscle afferent transmission within the spinal dorsal horn and modulation of barosensory processing within the nucleus of the solitary tract. At the integrative level, studies of the arterial baroreflex, exercise pressor reflex, and trigeminally mediated diving reflex further demonstrate that central command can selectively regulate reflex expression and modify interactions among competing autonomic inputs rather than summating with them. Together, these observations support a conceptual evolution of central command from a feedforward initiator to a hierarchical autonomic organizing system (CHAOS) that dynamically integrates central command with peripheral cardiovascular feedback according to behavioral context. This CHAOS framework provides a unifying physiological interpretation for findings traditionally considered within individual reflex systems and offers a broader perspective on cardiovascular regulation during exercise and other conditions imposing competing autonomic demands.